今天的分享，要从一个故事开始。。。
-
我们总会有这种瞬间，就是在不经意间，会捕捉到一个感兴趣、并且自认为值得研究的观点，希望开展更加深入地研究。比如，我们在参加学术会议时，偶然听到某知名学者讲到的一篇论文；我们在阅读文献时，忽然看到浏览器推荐的一篇文献；我们使用文献管理工具如Mendely、Zotero时，邮箱中不定期收到的推送文章，等等。就在那一瞬间，一个刺耳的词汇冲进耳朵，亦或者，一个醒目的词汇映入眼帘，仿佛那时心中窃喜一个伟大的研究成果正在蓬勃孕育，并且即将要诞生。。。
-
好了，故事讲到这里。
【PS：小编午休睡醒了。。。
】
-
那么问题来了：当我们听到一个新的词汇、亦或一篇感兴趣的文章时，我们该如何深入地、快速挖掘出相关研究成果呢？毕竟，一个词汇或者一篇文献不足以支撑一篇有价值的成果诞生。
-
望梅止渴：你与SCI/SSCI/CSSCI/CSCD论文之间的距离，只间隔了一个『神器』！
神器名称：
神器网址：
你可以实现以下功能：
-
Get a visual overview of a new academic field
Enter a typical paper and we'll build you a graph of similar papers in the field. Explore and build more graphs for interesting papers that you find - soon you'll have a real, visual understanding of the trends, popular works and dynamics of the field you're interested in.
-
Make sure you haven't missed an important paper
In some fields like Machine Learning, so many new papers are published it's hard to keep track. With Connected Papers you can just search and visually discover important recent papers. No need to keep lists.
-
Create the bibliography to your thesis
Start with the references that you will definitely want in your bibliography and use Connected Papers to fill in the gaps and find the rest!
-
Discover the most relevant prior and derivative works
Use our Prior Works view to find important ancestor works in your field of interest. Use our Derivative Works view to find literature reviews of the field, as well as recently published State of the Art that followed your input paper.
自身体验：
小编以自身从事的研究方向“Causal Inference”（因果推断）为检索词进行检索（也可以用DOI号、文章标题、URL等做精准检索），在检索结果里选择感兴趣的一篇文献，点击“Build a graph”，整个图谱生成速度受到所选择的文章参考文献数量及被引量的影响，整体上很快。
网页超级贴心，怕你看不懂图谱，还告诉你怎么解读图谱。
【点击图谱左下角“问号”】弹出：
Each node is an academic paper related to the origin paper.
-
Papers are arranged according to their similarity (this is not a citation tree) -
Node size is the number of citations -
Node color is the publishing year -
Similar papers have strong connecting lines and cluster together
而且，你可以查看你检索文章的“先前作品”与“衍生作品”，从而可以准确把握你所关注一篇文章的来龙去脉，似不似很实用呀？！
还等什么，赶快去动手试一试吧！
『往期好文速览』猛戳超链接↓
005. SCI/SSCI『2020影响因子』出炉 | 附: 近五年对比数据
010. 『号外号外』WoS数据库更新后导入VOSviewer出错的原因
011. 『重磅福利』提供WoS数据C1和EM重复字段清洗服务
欢迎关注“因果关系推断”微信公众号，定期分享科研干货，无需转发，诚意满满！学术征途，有我相伴！由于微信公众号改版，为防止失联，敬请星标公众号，抱拳了，老铁们！